I want to train it further. If I immediately try to fit_one_cycle, I get an error since my learn.dls is empty. This makes sense, it’s not going to save my whole dataset when I export the model. My question is how do I ‘attach’ my original dataset to my dataloaders object in my learner so that I can train it further?
Presumably, I don’t need to re-define my entire dls? Or do I? (my data is a dataframe of image file names and labels) Thanks in advance for any help!
Can I not extract the original dataloaders from the learner? For example, I can do learn.dls.test_dl(new_data) to apply the same transforms and normalizations on a test data set, surely this means the dataloaders is stored in the learner object?
My dataset has changed since I first trained the model, but I want to keep the original normalize values (from when I first trained the model), which I cannot do if I have to redefine a new dataloaders object with new data. Do you see the problem?
We are assuming that the labels are stored in the exact same way as they were grabbed via training. IE if the labels are grabbed via parent_label, then they must be that way here as well
Not 100% sure this will work perfectly, I’m pretty sure it should as all transforms are in both dataloaders, and which are applied and how is based on split_idx, so make sure to play with it and verify. As test_dl builds a new validation DataLoader specifically